Neural networks, or better artificial neural networks (ANN)
are a family of algorithms especially usefull for classification
and function approximation. Their special importance lies in
the fact that they provide a versatile way to represent a general
nonlinear mapping between multidimensional spaces.

There are lots of different types of artificial neural networks
and training methods to optimize their parameters. One common
way to categorize artificial neural networks is the distinction
between supervised and unsupervised learning. In supervised
learning the mapping of some input to some known ouput is learnt
and the better the output is predicted the better the ANN performs.
In unsupervised learning, on the other hand, the network relies
entirely on the input data without reference to any ouptut data.
Here, the goal is normally to model the probability distribution
of the data or to discover clusters or other structure.

Applications of unsupervised as well as supervised methods
to the analysis of biological activity data are presented. Among
the multitude of available methods Self-Organizing Feature Maps
(Kohonen Networks) and Multi-layer Perceptrons are discussed
in depth.